Manifold learning for image-based breathing gating in MRI

Respiratory motion is a challenging factor for image-guided procedures in the abdominal region. Target localization, an important issue in applications like radiation therapy, becomes difficult due to this motion. Therefore, it is necessary to detect the respiratory signal to have a higher accuracy in planning and treatment. We propose a novel image-based breathing gating method to recover the breathing signal directly from the image data. For the gating we use Laplacian eigenmaps, a manifold learning technique, to determine the low-dimensional manifold embedded in the high-dimensional space. Since Laplacian eigenmaps assign each 2D MR slice a coordinate in a low-dimensional space by respecting the neighborhood relationship, they are well suited for analyzing the respiratory motion. We perform the manifold learning on MR slices acquired from a fixed location. Then, we use the resulting respiratory signal to derive a similarity criterion to be used in applications like 4D MRI reconstruction. We perform experiments on liver data using one and three dimensions as the dimension of the manifold and compare the results. The results from the first case show that using only one dimension as the dimension of the manifold is not enough to represent the complex motion of the liver caused by respiration. We successfully recover the changes due to respiratory motion by using three dimensions. The proposed method has the potential of reducing the processing time for the 4D reconstruction significantly by defining a search window for a subsequent registration approach. It is fully automatic and does not require any prior information or training data.